Association or Causation: Evaluating Links between "Environment and Disease"

Article excerpt

Epidemiological studies typically examine associations between an exposure variable and a health outcome. In assessing the causal nature of an observed association, the "Bradford Hill criteria" have long provided a background framework--in the words of one of Bradford Hill's closest colleagues, an "aid to thought" (1). First published exactly 40 years ago, these criteria also provided biomedical relevance to epidemiological research and quickly became a mainstay of epidemiological textbooks and data interpretation (2). Their checklist nature suited the study of simple, direct causation by disciplines characterized by classic scientific and mathematical training.

Most diseases have a multifactorial pathogenesis, but the conceptualization of their causation varies by discipline. While it is scientifically satisfying to elucidate the many component causes of an illness, in public health research the more important emphasis is on the discovery of necessary or sufficient causes that are amenable to intervention. Even so, over the four decades since Bradford Hill's paper appeared, the range of multivariate, multistage and multi-level research questions tackled by epidemiologists has evolved, as have their statistical methods and their engagement in wider-ranging interdisciplinary research. Within that context it is often not appropriate to seek the discrete cause or causes of a disease, but rather to identify a complex of interrelated and often interacting factors that influence the risk of disease (1). This complicates the assessment of causality.

The general context within which Bradford Hill developed his ideas about causal inference warrants brief review here. Most epidemiological research is non-experimental, being conducted in an inherently "noisy" environment in free-living populations. For example, the quality of the measurement of exposure and of health status is usually less than in controlled clinical trials or laboratory-based studies (measurement error); there are potential confounding variables that are statistically associated with the exposure variable of interest while also predictive of the health outcome in their own right, and these covariates must be controlled for; the sample of persons studied may not provide true information about the relationship between exposure and outcome in the source population, with respect either to the relationship that the sample actually displays (selection bias) or apparently displays (classification bias). Epidemiologists therefore seek research settings and study designs that maximize the signal-to-noise ratio.

These sources of noise, intrinsic to much epidemiological research, require one to proceed cautiously in making causal inference. Once sufficient studies have been done, in diverse settings, and adequately limiting random error (an intrinsic property of a stochastic universe), systematic error (bias) and logical error (confounding), then the causal nature of observed associations can reasonably be assessed.

Note, though, that particular phrase: "causal nature". Causation is an interpretation, not an entity; it should not be reified. The 18th-century Scottish philosopher David Hume pointed out that causation is induced logically, not observed empirically (3). Therefore we can never know absolutely that exposure X causes disease Y. There is no final proof of causation: it is merely an inference based on an observed conjunction of two variables (exposure and health status) in time and space. This limitation of inductive logic applies, of course, to both experimental and non-experimental research.

Around the mid-20th century, the philosopher Karl Popper offered a solution to this problem of reliance on induction. He stressed that science progresses by rejecting or modifying causal hypotheses, not by actually proving causation. While flirting briefly with Popper's ideas in the 1970s (4), epidemiologists have generally taken a practical data-based approach to the notion of causation, comfortably embracing Bradford Hill's criteria of causality. …